Development of an Empirically Derived Measure of Food Safety Culture in Restaurants.

A poor food safety culture has been described as an emerging risk factor for foodborne illness outbreaks, yet there has been little research on this topic in the retail food industry. The purpose of this study was to identify and validate conceptual domains around food safety culture and develop an assessment tool that can be used to assess food workers' perceptions of their restaurant's food safety culture. The study, conducted from March 2018 through March 2019, surveyed restaurant food workers for their level of agreement with 28 statements. We received 579 responses from 331 restaurants spread across eight different health department jurisdictions. Factor analysis and structural equation modeling supported a model composed of four primary constructs. The highest rated construct was Resource Availability (x¯=4.69, sd=0.57), which assessed the availability of resources to maintain good hand hygiene. The second highest rated construct was Employee Commitment (x¯=4.49, sd=0.62), which assessed workers' perceptions of their coworkers' commitment to food safety. The last two constructs were related to management. Leadership (x¯=4.28, sd=0.69) assessed the existence of food safety policies, training, and information sharing. Management Commitment (x¯=3.94, sd=1.05) assessed whether food safety was a priority in practice. Finally, the model revealed one higher-order construct, Worker Beliefs about Food Safety Culture (x¯=4.35, sd=0.53). The findings from this study can support efforts by the restaurant industry, food safety researchers, and health departments to examine the influence and effects of food safety culture within restaurants.

Restaurant Date-Marking Practices Concerning Ready-to-Eat Food Requiring Time and Temperature Control for Safety.

Certain foods are more vulnerable to foodborne pathogen growth and formation of toxins than others. Lack of time and temperature control for these foods can result in the growth of pathogens, such as Listeria monocytogenes, and lead to foodborne outbreaks. The Food and Drug Administration's (FDA) Food Code classifies these foods as time/temperature control for safety (TCS) foods and details safe cooking, holding, and storing temperatures for these foods. The FDA Food Code also includes a date-marking provision for ready-to-eat TCS foods that are held for >24 h. The provision states that these foods should not be held in refrigeration for >7 days and should be marked with the date or day by which the food should be "consumed on the premises, sold, or discarded." To learn more about restaurants' date-marking practices, the Centers for Disease Control and Prevention's Environmental Health Specialists Network (EHS-Net) conducted observations and manager interviews in 359 restaurants in 8 EHS-Net jurisdictions. Managers reported that they date marked ready-to-eat TCS foods more often than data collectors observed this practice (91% vs. 77%). Observation data showed almost a quarter of study restaurants did not date-mark ready-to-eat TCS foods. In addition, restaurants with an internal date-marking policy date marked 1.25 times more often than restaurants without such a policy and chain restaurants date marked 5.02 times more often than independently owned restaurants. These findings suggest that regulators and the retail food industry may improve food safety and lower the burden of foodborne illness in the United States if they target interventions to independent restaurants and encourage strong date-marking policies.

Machine-learned epidemiology: real-time detection of foodborne illness at scale.

Machine learning has become an increasingly powerful tool for solving complex problems, and its application in public health has been underutilized. The objective of this study is to test the efficacy of a machine-learned model of foodborne illness detection in a real-world setting. To this end, we built FINDER, a machine-learned model for real-time detection of foodborne illness using anonymous and aggregated web search and location data. We computed the fraction of people who visited a particular restaurant and later searched for terms indicative of food poisoning to identify potentially unsafe restaurants. We used this information to focus restaurant inspections in two cities and demonstrated that FINDER improves the accuracy of health inspections; restaurants identified by FINDER are 3.1 times as likely to be deemed unsafe during the inspection as restaurants identified by existing methods. Additionally, FINDER enables us to ascertain previously intractable epidemiological information, for example, in 38% of cases the restaurant potentially causing food poisoning was not the last one visited, which may explain the lower precision of complaint-based inspections. We found that FINDER is able to reliably identify restaurants that have an active lapse in food safety, allowing for implementation of corrective actions that would prevent the potential spread of foodborne illness.